Searching for Collective Behavior in a Large Network of Sensory Neurons

To refer to this page use:
http://arks.princeton.edu/ark:/88435/pr13s44

Abstract:

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics
measured in an interacting network. Here we use this principle to construct probabilistic models which describe the
correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies.
Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in
an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global
interaction that controls the distribution of synchrony in the population. Here we show that such ‘‘K-pairwise’’ models—
being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We
explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population’s capacity to
represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that
the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is
highly predictable from the rest of the population, allowing the capacity for error correction.